# Sensor Fusion and State Estimation of IoT Enabled Wind Energy Conversion System

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## Abstract

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## 1. Introduction

## 2. IoT Enabled Wind Energy Conversion System and State Space Model

## 3. Proposed Communication Framework

#### Repeat Accumulate (RA) Codes

#### Belief Propagation Decoding

- ${\mathbb{S}}_{v}(m)$ → set of variable nodes that have connection/edge with the ${m}^{th}$ check node.
- ${\mathbb{S}}_{c}(m)$ → set of check nodes that have connection/edge with the ${m}^{th}$ variable node.
- ${\mathcal{V}}_{n,m}^{(\ell )}$ → LLR message sent from variable node m to check node n at iteration ℓ.
- ${\mathcal{C}}_{n,m}^{(\ell )}$ → LLR message sent from check node n to variable node m at iteration ℓ.

**Message from check node:**

**Message from variable node:**

## 4. Proposed Sensor Fusion Technique

**Step 1—Prediction:**Let ${\widehat{\mathit{X}}}^{-}(t)$ and ${\widehat{\mathit{P}}}^{-}(t)$ be the predicted state and co-variance matrix, respectively. According to the Kalman filter algorithm, we calculate ${\widehat{\mathit{X}}}^{-}(t)$ and ${\widehat{\mathbf{{\rm Y}}}}^{-}(t)$ by

**Step 2—Modification:**We find the summed syndrome value of each component of ${\widehat{{\mathcal{O}}}}_{i}(t)$. We remove the erroneous component from ${\widehat{{\mathcal{O}}}}_{i}(t)$ and modify ${\mathit{C}}_{i}$ and ${\mathit{R}}_{i}$ accordingly. Let ${\overline{{\mathcal{O}}}}_{i}(t)$, ${\overline{\mathit{C}}}_{i}(t)$, and ${\overline{\mathit{R}}}_{i}(t)$ be the modified version of ${\widehat{{\mathcal{O}}}}_{i}(t)$, ${\mathit{C}}_{i}$ and ${\mathit{R}}_{i}$, respectively.

**Step 3—Update:**Let ${\mathit{\u03f5}}_{i}(t)$ and ${{\mathcal{G}}}_{i}(t)$ be the measurement pre-fit residual and Kalman gain, respectively. We calculate ${\mathit{\u03f5}}_{i}(t)$ and ${{\mathcal{G}}}_{i}(t)$ by

**Step 4—Fusion:**Let ${\mathbf{\Phi}}_{pq}(t)$, $p,q\in \{1,2,\dots ,N\}$ be the error cross co-variance between the ${p}^{th}$ and the ${q}^{th}$ sensors. ${\mathbf{\Phi}}_{pq}(t)$ is given by

## 5. Performance Evaluations

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Use of Internet of Things (IoT) network for transmitting data from turbines to control center and technicians.

**Figure 3.**A Tanner graph representation of a repeat accumulate code with $(q,a)=(3,3)$. In the graph, filled and unfilled circle nodes represent the information and parity bits, respectively, while rectangular nodes represent check equations.

Works | Type of Wind Turbine | Filter Type | Sensor Fusion | Impact of Wireless Channel | Error Correction Technique |
---|---|---|---|---|---|

Berg et al. [6] | Generic | Linear Kalman | No | No | No |

Ritter et al. [8] | Generic | Linear Kalman | No | No | No |

Petar et al. [9] | Generic | Extended Kalman | No | No | No |

Bourlis et al. [10] | Generic | Adaptive Kalman | No | No | No |

Blanco et al. [11] | Generic | Extended Kalman | No | No | No |

Sudev et al. [12] | Generic | Particle filter | No | No | No |

Yu et al. [13] | DFIG | Unscented Kalman | No | No | No |

Yu et al. [14] | DFIG | Unscented Kalman | No | No | No |

Prajapat et al. [15] | DFIG | Unscented Kalman | No | No | No |

Shahriari et al. [16] | PMSG | Extended Kalman | No | No | No |

This work | Generic | Linear Kalman | Yes | Yes | Yes |

Parameter | Value |
---|---|

Base frequency | 10 Hz |

Stator frequency | 15 Hz |

Rotor frequency | 15 Hz |

Resistance of stator | 0.004 Ω |

Resistance of rotor | 0.005 Ω |

Reactance of stator | 0.09 Ω |

Reactance of rotor | 0.08 Ω |

Magnetizing reactance | 3.95 Ω |

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**MDPI and ACS Style**

Noor-A-Rahim, M.; Khyam, M.O.; Li, X.; Pesch, D. Sensor Fusion and State Estimation of IoT Enabled Wind Energy Conversion System. *Sensors* **2019**, *19*, 1566.
https://doi.org/10.3390/s19071566

**AMA Style**

Noor-A-Rahim M, Khyam MO, Li X, Pesch D. Sensor Fusion and State Estimation of IoT Enabled Wind Energy Conversion System. *Sensors*. 2019; 19(7):1566.
https://doi.org/10.3390/s19071566

**Chicago/Turabian Style**

Noor-A-Rahim, Md., M. O. Khyam, Xinde Li, and Dirk Pesch. 2019. "Sensor Fusion and State Estimation of IoT Enabled Wind Energy Conversion System" *Sensors* 19, no. 7: 1566.
https://doi.org/10.3390/s19071566